Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box. Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates state-of-the-art diagnostics that can be applied more generally and can be incorporated into the software of others.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  2. Murray, Jared S.: Multiple imputation: a review of practical and theoretical findings (2018)
  3. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  4. Fung, Dennis; Kutnick, Peter; Mok, Ida; Leung, Frederick; Pok-Yee Lee, Betty; Mai, Yee Yan; Tyler, Matthew Telford: Relationships between teachers’ background, their subject knowledge and pedagogic efficacy, and pupil achievement in primary school mathematics in Hong Kong: an indicative study (2017) MathEduc
  5. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  6. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
  7. Nguyen, Cattram D.; Lee, Katherine J.; Carlin, John B.: Posterior predictive checking of multiple imputation models (2015)
  8. Rashid, S.; Mitra, R.; Steele, R. J.: Using mixtures of (t) densities to make inferences in the presence of missing data with a small number of multiply imputed data sets (2015)
  9. Daniel Oberski: lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models (2014) not zbMATH
  10. Liu, Jingchen; Gelman, Andrew; Hill, Jennifer; Su, Yu-Sung; Kropko, Jonathan: On the stationary distribution of iterative imputations (2014)
  11. van Ginkel, Joost R.; Kroonenberg, Pieter M.: Using generalized Procrustes analysis for multiple imputation in principal component analysis (2014)
  12. Li, Yuelin; Baron, Jonathan: Behavioral research data analysis with R (2012)
  13. Zajonc, Tristan: Bayesian inference for dynamic treatment regimes: mobility, equity, and efficiency in student tracking (2012)
  14. Drechsler, Jörg: Synthetic datasets for statistical disclosure control. Theory and implementation (2011)
  15. Drechsler, Jörg: Multiple imputation in practice -- a case study using a complex German establishment survey (2011)
  16. Larsen, Michael D.: Discussion of “Calibrated Bayes, for statistics in general, and missing data in particular” by R.J.A. Little (2011)
  17. Recai Yucel: State of the Multiple Imputation Software (2011) not zbMATH
  18. Stef van Buuren; Karin Groothuis-Oudshoorn: mice: Multivariate Imputation by Chained Equations in R (2011) not zbMATH
  19. Yu-Sung Su; Andrew Gelman; Jennifer Hill; Masanao Yajima: Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box (2011) not zbMATH
  20. White, Ian R.; Daniel, Rhian; Royston, Patrick: Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables (2010)